1. Field of the Invention
The invention relates to a data compression method which is put into effect with the aid of:
an encoder which effects a transform with the aid of a coding neural network provided at the transmission end, having a number of input neurons greater than or equal to the number of output neurons, PA1 and a decoder which comprises a decoding neural network which is matched to the coding neural network positioned at the receiving end and effects almost the inverse transform of the encoder with a number of input neurons less than or equal to the number of output neurons, the method comprising the following steps: PA1 learning by the coding and decoding neural networks by adapting their synaptic coefficients to the example of the blocks of input data, PA1 encoding data blocks to be processed, PA1 the transmission of the encoded data via a channel, PA1 decoding of the encoded data received by the matched decoding neural network and formation of the recovered data blocks. PA1 a N-neuron input layer which receives an input data block at each instant, PA1 a P-neuron hidden layer wherein P&lt;N whose outputs which are quantised over Q bits represent the compressed information, PA1 a N-neuron output layer to recover an output data block.
It also relates to an encoder and a decoder which put such a method into effect. The method can be employed in a structure in which an encoder, a decoder and a transmission signal are combined.
2. Description of the Related Art
A structure of this kind is described in the document: "Image compression by back propagation: an example of extensional programming" by G. W. COTTRELL, P. MUNRO and D. ZIPSET in "Advances in Cognitive Science Vol. 3, 1987, Norwood N.J, Editor: N. E. SHARKEY.
This document describes an image compression technique which puts two associated neural networks into operation, one serving for the encoding which transforms the input data into encoded data and the other serving for the decoding which effects an inverse mechanism. It is necessary to teach examples to the coding and decoding neural networks and for that purpose to modify their synaptic coefficients so as to ensure that the encoded data are in conformity with the data anticipated for the predetermined types of examples. This teaching can be effected in accordance with the known gradient back propagation technique or the known technique of analysis of principal components. The encoded data are transmitted via a channel, to a decoder which includes a decoding neural network which performs the inverse mechanism. To that end, synaptic coefficients which are determined after the learning step has ended, are loaded in the decoding neural network. The structure can then operate with data originating from an image to be processed.
Such a structure is composed of three layers when the coding and decoding neural networks are combined:
But such a structure must be adapted for a given type of texture and thereafter will process all the blocks of the same image and all the consecutive images with the same synaptic coefficients. Thus, images having image structures or zone structures which are very different as regards their uniformity or their non-uniformity, will be processed in the same manner. Consequently, a coding could be optimum for one type of texture and be poorly adapted to an other type of texture.